class_docstr: Clips the target after predicting.
  
  Parameters
  ----------
  regressor
      Regressor model for which to clip the predictions.
  y_min
      minimum value.
  y_max
      maximum value.
  
  Examples
  --------
  >>> from river import linear_model
  >>> from river import preprocessing
  
  >>> dataset = (
  ...     ({'a': 2, 'b': 4}, 80),
  ...     ({'a': 3, 'b': 5}, 100),
  ...     ({'a': 4, 'b': 6}, 120)
  ... )
  
  >>> model = preprocessing.PredClipper(
  ...     regressor=linear_model.LinearRegression(),
  ...     y_min=0,
  ...     y_max=200
  ... )
  
  >>> for x, y in dataset:
  ...     model.learn_one(x, y)
  
  >>> model.predict_one({'a': -100, 'b': -200})
  0
  
  >>> model.predict_one({'a': 50, 'b': 60})
  200
code: def __init__(
      self, regressor: base.Regressor, y_min: float, y_max: float
  ):
      self.regressor = regressor
      self.y_min = y_min
      self.y_max = y_max
  
file_path: river/river/preprocessing/pred_clipper.py
imports: from __future__ import annotations
  from river import base, utils
label_desc: Use image node_id 1 to create a new PredClipper object from inherited base classes: base, base with example: obj = PredClipper(regressor, y_min, y_max)